An Incentive Mechanism Based on Behavioural Economics in Location-based Crowdsensing Considering an Uneven Distribution of Participants

Abstract

The location of participants in Location-based CrowdSensing (LCS) represents important information for task completion. Tasks in areas with high concentration of participants (AHCP) can be completed quickly, whereas task completion is difficult in areas with sparse participants (ASP). Incentive mechanisms are necessary to motivate participants to move toward ASP. Previous studies have faced two main problems. First, most incentive mechanisms assume that participant motivation is not affected by external factors. Second, when participants fail to complete tasks, only the cost of the participant is considered the loss. However, reference effect from behavioral economics proves that participants are influenced by both internal and external factors. Furthermore, loss aversion studies have shown that participant evaluations of loss are more severe than simple costs. Therefore we propose an incentive mechanism based on behavioral economics (IBE) consisting of two schemes for participant selection (IBE-PS) and payment decisions (IBE-PD). Based on reference effect, IBE-PS is proposed to control the task selection and pricing of participants. Based on loss aversion, IBE-PD is proposed to encourage participants to complete tasks in ASP many times. Theoretical analysis and simulation results demonstrate that IBE can improve the task completion rate, the participant utility, and the platform welfare.